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Record W7087274972 · doi:10.5281/zenodo.17312404

Cross-site comparison of ecosystem- and plot-scale methane fluxes from wetlands and uplands (Version 1)

2025· dataset· en· W7087274972 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsUniversité de MontréalMcMaster UniversityDalhousie UniversityMcGill University
Fundersnot available
KeywordsEddy covarianceMethane emissionsMethaneFlux (metallurgy)WetlandAtmospheric methane

Abstract

fetched live from OpenAlex

This dataset contains the six temporal aggregations (separate datasets; half-hourly, hourly, daily, weekly, monthly and annual) used in the article "Määttä et al. A cross-site comparison of ecosystem- and plot-scale methane fluxes from wetlands and uplands" (manuscript submitted to Biogeosciences 2025-10-10). The datasets contain contemporaneous timestamp-aligned methane (CH4) flux data from eddy covariance (EC) and chamber measurements from 10 sites, as well as environmental data derived from FLUXNET-CH4 (Delwiche et al., 2020; Knox et al., 2019; https://fluxnet.org/data/fluxnet-ch4-community-product/) and chamber-associated measurements used as predictors of ecosystem and plot-scale CH4 flux differences. Please note that all (except for site US-StJ) EC data were obtained from the FLUXNET-CH4 database (Delwiche et al., 2020; Knox et al., 2019; CC-BY 4.0) and chamber CH4 flux and environmental data from site data providers (chamber data that has not been published elsewhere with CC-BY 4.0 has been included in these datasets with permission from data providers). EC data for US-StJ were obtained from the site data providers. For questions related to site-specific chamber and EC CH4 flux and environmental data, please contact the data providers or refer to site-specific literature (see README.txt).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.005
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.262
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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